Learning to Recognize Pedestrian Attribute

TL;DR

This paper introduces a novel method for recognizing pedestrian attributes from far-distance images by leveraging neighboring context and evaluates feature importance using a new, large-scale dataset.

Contribution

It proposes an alternative approach using contextual information and provides extensive analysis on feature informativeness with a new comprehensive dataset.

Findings

Contextual information improves attribute recognition accuracy.

Background and foreground features have different levels of informativeness.

The new dataset is the largest and most diverse for pedestrian attribute recognition.

Abstract

Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments are based on our newly released pedestrian attribute dataset, which is by far the largest and most diverse of its kind.